fft_lpf.py

import numpy as np
import cv2
import matplotlib.pyplot as plt



# read input and convert to grayscale
image_path = "C://Users//anhpn//Desktop//MEMS//code2//img//frame177.jpg"

img = cv2.imread(image_path)

# do dft saving as complex output
dft = np.fft.fft2(img, axes=(0,1))

# apply shift of origin to center of image
dft_shift = np.fft.fftshift(dft)

# generate spectrum from magnitude image (for viewing only)
mag = np.abs(dft_shift)
spec = np.log(mag) / 20

# create circle mask
radius = 50
mask = np.zeros_like(img)
cy = mask.shape[0] // 2
cx = mask.shape[1] // 2
cv2.circle(mask, (cx,cy), radius, (255,255,255), -1)[0]

# blur the mask
mask2 = cv2.GaussianBlur(mask, (19,19), 0)

# apply mask to dft_shift
dft_shift_masked = np.multiply(dft_shift,mask) / 255
dft_shift_masked2 = np.multiply(dft_shift,mask2) / 255

# shift origin from center to upper left corner
back_ishift = np.fft.ifftshift(dft_shift)
back_ishift_masked = np.fft.ifftshift(dft_shift_masked)
back_ishift_masked2 = np.fft.ifftshift(dft_shift_masked2)

# do idft saving as complex output
img_back = np.fft.ifft2(back_ishift, axes=(0,1))
img_filtered = np.fft.ifft2(back_ishift_masked, axes=(0,1))
img_filtered2 = np.fft.ifft2(back_ishift_masked2, axes=(0,1))

# combine complex real and imaginary components to form (the magnitude for) the original image again
img_back = np.abs(img_back).clip(0,255).astype(np.uint8)
img_filtered = np.abs(img_filtered).clip(0,255).astype(np.uint8)
img_filtered2 = np.abs(img_filtered2).clip(0,255).astype(np.uint8)

plt.figure()
plt.subplot(221)
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.title("Original")


plt.subplot(222)
plt.imshow(spec)
plt.title("Spectrum")

plt.subplot(223)
plt.imshow(mask)
plt.title("Mask")

plt.subplot(224)
plt.imshow(cv2.cvtColor(img_filtered, cv2.COLOR_BGR2RGB))
plt.title("Result")
plt.show()